AutoML Leaderboard
| Best model | name | model_type | metric_type | metric_value | train_time | single_prediction_time |
|---|---|---|---|---|---|---|
| 1_Linear | Linear | logloss | 0.192042 | 14.19 | 0.042 | |
| 2_Default_LightGBM | LightGBM | logloss | 0.13902 | 5.93 | 0.0168 | |
| 3_Default_Xgboost | Xgboost | logloss | 0.1365 | 8.05 | 0.016 | |
| 4_Default_CatBoost | CatBoost | logloss | 0.125332 | 5.19 | 0.0165 | |
| 5_Default_NeuralNetwork | Neural Network | logloss | 0.261498 | 4.51 | 0.041 | |
| 6_Default_RandomForest | Random Forest | logloss | 0.236473 | 7.58 | 0.165 | |
| 11_LightGBM | LightGBM | logloss | 0.152876 | 6.06 | 0.0135 | |
| 7_Xgboost | Xgboost | logloss | 0.171097 | 6.54 | 0.0158 | |
| 15_CatBoost | CatBoost | logloss | 0.12676 | 8.29 | 0.017 | |
| 19_RandomForest | Random Forest | logloss | 0.197129 | 10.01 | 0.1383 | |
| 23_NeuralNetwork | Neural Network | logloss | 0.232964 | 5.38 | 0.0523 | |
| 12_LightGBM | LightGBM | logloss | 0.130602 | 6.08 | 0.012 | |
| 8_Xgboost | Xgboost | logloss | 0.13694 | 6.03 | 0.0188 | |
| 16_CatBoost | CatBoost | logloss | 0.129666 | 7.96 | 0.0144 | |
| 20_RandomForest | Random Forest | logloss | 0.230631 | 9.4 | 0.1384 | |
| 24_NeuralNetwork | Neural Network | logloss | 0.192402 | 6.17 | 0.0421 | |
| 13_LightGBM | LightGBM | logloss | 0.140203 | 6.97 | 0.0131 | |
| 9_Xgboost | Xgboost | logloss | 0.381057 | 6.72 | 0.0181 | |
| 17_CatBoost | CatBoost | logloss | 0.120144 | 7.32 | 0.0138 | |
| 21_RandomForest | Random Forest | logloss | 0.281769 | 10.33 | 0.1615 | |
| 25_NeuralNetwork | Neural Network | logloss | 0.244839 | 6.8 | 0.0351 | |
| 14_LightGBM | LightGBM | logloss | 0.134268 | 8.15 | 0.0129 | |
| 10_Xgboost | Xgboost | logloss | 0.693123 | 6.15 | 0.0157 | |
| 18_CatBoost | CatBoost | logloss | 0.128354 | 8.59 | 0.0212 | |
| 22_RandomForest | Random Forest | logloss | 0.282002 | 10.83 | 0.1673 | |
| 26_NeuralNetwork | Neural Network | logloss | 0.190663 | 6.95 | 0.0432 | |
| 17_CatBoost_GoldenFeatures | CatBoost | logloss | 0.123813 | 8.65 | 0.0347 | |
| 4_Default_CatBoost_GoldenFeatures | CatBoost | logloss | 0.123543 | 8.29 | 0.048 | |
| 15_CatBoost_GoldenFeatures | CatBoost | logloss | 0.129454 | 10.4 | 0.0416 | |
| 17_CatBoost_RandomFeature | CatBoost | logloss | 0.125614 | 9.27 | 0.0223 | |
| 17_CatBoost_SelectedFeatures | CatBoost | logloss | 0.11511 | 8.66 | 0.0148 | |
| 12_LightGBM_SelectedFeatures | LightGBM | logloss | 0.126824 | 8.88 | 0.0197 | |
| 3_Default_Xgboost_SelectedFeatures | Xgboost | logloss | 0.135841 | 8.68 | 0.0303 | |
| 26_NeuralNetwork_SelectedFeatures | Neural Network | logloss | 0.159097 | 7.93 | 0.0448 | |
| 19_RandomForest_SelectedFeatures | Random Forest | logloss | 0.21026 | 11.56 | 0.1325 | |
| 27_CatBoost_SelectedFeatures | CatBoost | logloss | 0.115485 | 8.83 | 0.0219 | |
| 28_CatBoost_SelectedFeatures | CatBoost | logloss | 0.12736 | 10.02 | 0.017 | |
| 29_CatBoost | CatBoost | logloss | 0.120864 | 9.01 | 0.0177 | |
| 30_LightGBM_SelectedFeatures | LightGBM | logloss | 0.126824 | 9.11 | 0.0138 | |
| 31_LightGBM_SelectedFeatures | LightGBM | logloss | 0.126824 | 9.07 | 0.0123 | |
| 32_LightGBM | LightGBM | logloss | 0.130602 | 9.15 | 0.0137 | |
| 33_LightGBM | LightGBM | logloss | 0.130602 | 9.24 | 0.0136 | |
| 34_Xgboost_SelectedFeatures | Xgboost | logloss | 0.138084 | 9.43 | 0.0212 | |
| 35_Xgboost_SelectedFeatures | Xgboost | logloss | 0.13483 | 9.38 | 0.0166 | |
| 36_Xgboost | Xgboost | logloss | 0.136081 | 10.38 | 0.016 | |
| 37_Xgboost | Xgboost | logloss | 0.137424 | 9.55 | 0.0146 | |
| 38_NeuralNetwork_SelectedFeatures | Neural Network | logloss | 0.189926 | 9.05 | 0.0358 | |
| 39_RandomForest | Random Forest | logloss | 0.210377 | 12.53 | 0.1249 | |
| 40_RandomForest_SelectedFeatures | Random Forest | logloss | 0.211415 | 13.5 | 0.1831 | |
| 41_CatBoost_SelectedFeatures | CatBoost | logloss | 0.114777 | 11.26 | 0.0229 | |
| 42_CatBoost_SelectedFeatures | CatBoost | logloss | 0.122772 | 10.36 | 0.0184 | |
| 43_CatBoost_SelectedFeatures | CatBoost | logloss | 0.118655 | 11.15 | 0.0237 | |
| 44_CatBoost_SelectedFeatures | CatBoost | logloss | 0.111754 | 10.41 | 0.0159 | |
| 45_LightGBM_SelectedFeatures | LightGBM | logloss | 0.130249 | 10.81 | 0.0148 | |
| 46_LightGBM_SelectedFeatures | LightGBM | logloss | 0.130249 | 11.08 | 0.0125 | |
| 47_Xgboost_SelectedFeatures | Xgboost | logloss | 0.165658 | 12.23 | 0.0175 | |
| 48_Xgboost_SelectedFeatures | Xgboost | logloss | 0.163618 | 13.03 | 0.0176 | |
| 49_NeuralNetwork_SelectedFeatures | Neural Network | logloss | 0.203407 | 10.54 | 0.0333 | |
| 50_NeuralNetwork_SelectedFeatures | Neural Network | logloss | 0.178132 | 10.94 | 0.0357 | |
| 51_NeuralNetwork_SelectedFeatures | Neural Network | logloss | 0.249332 | 10.38 | 0.0332 | |
| 52_NeuralNetwork_SelectedFeatures | Neural Network | logloss | 0.206625 | 10.83 | 0.0441 | |
| 53_RandomForest | Random Forest | logloss | 0.198354 | 14.86 | 0.1326 | |
| 54_RandomForest_SelectedFeatures | Random Forest | logloss | 0.206369 | 13.63 | 0.1571 | |
| the best | Ensemble | Ensemble | logloss | 0.10497 | 15.52 | 0.1125 |
AutoML Performance
AutoML Performance Boxplot
Features Importance
Spearman Correlation of Models
Summary of 10_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.15
- max_depth: 8
- min_child_weight: 50
- subsample: 0.6
- colsample_bytree: 0.6
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.693123 | nan |
| auc | 0.503499 | nan |
| f1 | 0.666667 | 0.445146 |
| accuracy | 0.502183 | 0.494607 |
| precision | 0.505495 | 0.500126 |
| recall | 1 | 0.445146 |
| mcc | 0.005472 | 0.494607 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.693123 | nan |
| auc | 0.503499 | nan |
| f1 | 0.61745 | 0.494607 |
| accuracy | 0.502183 | 0.494607 |
| precision | 0.501362 | 0.494607 |
| recall | 0.803493 | 0.494607 |
| mcc | 0.005472 | 0.494607 |
Confusion matrix (at threshold=0.494607)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 46 | 183 |
| Labeled as 1 | 45 | 184 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 11_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.1 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.152876 | nan |
| auc | 0.982342 | nan |
| f1 | 0.960699 | 0.362657 |
| accuracy | 0.960699 | 0.362657 |
| precision | 1 | 0.988195 |
| recall | 1 | 1.07721e-05 |
| mcc | 0.921397 | 0.362657 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.152876 | nan |
| auc | 0.982342 | nan |
| f1 | 0.960699 | 0.362657 |
| accuracy | 0.960699 | 0.362657 |
| precision | 0.960699 | 0.362657 |
| recall | 0.960699 | 0.362657 |
| mcc | 0.921397 | 0.362657 |
Confusion matrix (at threshold=0.362657)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 12_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.130602 | nan |
| auc | 0.985832 | nan |
| f1 | 0.96 | 0.66659 |
| accuracy | 0.960699 | 0.66659 |
| precision | 1 | 0.993058 |
| recall | 1 | 0.000106007 |
| mcc | 0.92196 | 0.66659 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.130602 | nan |
| auc | 0.985832 | nan |
| f1 | 0.96 | 0.66659 |
| accuracy | 0.960699 | 0.66659 |
| precision | 0.977376 | 0.66659 |
| recall | 0.943231 | 0.66659 |
| mcc | 0.92196 | 0.66659 |
Confusion matrix (at threshold=0.66659)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 224 | 5 |
| Labeled as 1 | 13 | 216 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 12_LightGBM_SelectedFeatures
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.1 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.126824 | nan |
| auc | 0.987319 | nan |
| f1 | 0.965368 | 0.251195 |
| accuracy | 0.965066 | 0.251195 |
| precision | 1 | 0.991555 |
| recall | 1 | 4.46679e-05 |
| mcc | 0.930273 | 0.251195 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.126824 | nan |
| auc | 0.987319 | nan |
| f1 | 0.965368 | 0.251195 |
| accuracy | 0.965066 | 0.251195 |
| precision | 0.957082 | 0.251195 |
| recall | 0.973799 | 0.251195 |
| mcc | 0.930273 | 0.251195 |
Confusion matrix (at threshold=0.251195)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 219 | 10 |
| Labeled as 1 | 6 | 223 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 13_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
6.1 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.140203 | nan |
| auc | 0.984459 | nan |
| f1 | 0.962801 | 0.299151 |
| accuracy | 0.962882 | 0.299151 |
| precision | 1 | 0.988524 |
| recall | 1 | 0.000103851 |
| mcc | 0.925773 | 0.299151 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.140203 | nan |
| auc | 0.984459 | nan |
| f1 | 0.962801 | 0.299151 |
| accuracy | 0.962882 | 0.299151 |
| precision | 0.964912 | 0.299151 |
| recall | 0.960699 | 0.299151 |
| mcc | 0.925773 | 0.299151 |
Confusion matrix (at threshold=0.299151)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 14_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 95
- learning_rate: 0.05
- feature_fraction: 1.0
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.134268 | nan |
| auc | 0.985431 | nan |
| f1 | 0.960352 | 0.499963 |
| accuracy | 0.960699 | 0.499963 |
| precision | 1 | 0.99005 |
| recall | 1 | 0.00115879 |
| mcc | 0.921538 | 0.499963 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.134268 | nan |
| auc | 0.985431 | nan |
| f1 | 0.960352 | 0.499963 |
| accuracy | 0.960699 | 0.499963 |
| precision | 0.968889 | 0.499963 |
| recall | 0.951965 | 0.499963 |
| mcc | 0.921538 | 0.499963 |
Confusion matrix (at threshold=0.499963)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 222 | 7 |
| Labeled as 1 | 11 | 218 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 15_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.12676 | nan |
| auc | 0.990218 | nan |
| f1 | 0.961039 | 0.238035 |
| accuracy | 0.960699 | 0.238035 |
| precision | 1 | 0.974043 |
| recall | 1 | 0.000650805 |
| mcc | 0.921538 | 0.238035 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.12676 | nan |
| auc | 0.990218 | nan |
| f1 | 0.961039 | 0.238035 |
| accuracy | 0.960699 | 0.238035 |
| precision | 0.95279 | 0.238035 |
| recall | 0.969432 | 0.238035 |
| mcc | 0.921538 | 0.238035 |
Confusion matrix (at threshold=0.238035)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 218 | 11 |
| Labeled as 1 | 7 | 222 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 15_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.6 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.129454 | nan |
| auc | 0.988406 | nan |
| f1 | 0.960699 | 0.304845 |
| accuracy | 0.960699 | 0.304845 |
| precision | 1 | 0.952092 |
| recall | 1 | 0.00072947 |
| mcc | 0.921538 | 0.455795 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.129454 | nan |
| auc | 0.988406 | nan |
| f1 | 0.960699 | 0.304845 |
| accuracy | 0.960699 | 0.304845 |
| precision | 0.960699 | 0.304845 |
| recall | 0.960699 | 0.304845 |
| mcc | 0.921397 | 0.304845 |
Confusion matrix (at threshold=0.304845)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 16_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.129666 | nan |
| auc | 0.988921 | nan |
| f1 | 0.965066 | 0.309843 |
| accuracy | 0.965066 | 0.309843 |
| precision | 1 | 0.979239 |
| recall | 1 | 0.000123124 |
| mcc | 0.930273 | 0.389724 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.129666 | nan |
| auc | 0.988921 | nan |
| f1 | 0.965066 | 0.309843 |
| accuracy | 0.965066 | 0.309843 |
| precision | 0.965066 | 0.309843 |
| recall | 0.965066 | 0.309843 |
| mcc | 0.930131 | 0.309843 |
Confusion matrix (at threshold=0.309843)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 8 | 221 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 17_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
6.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.120144 | nan |
| auc | 0.990847 | nan |
| f1 | 0.967462 | 0.193248 |
| accuracy | 0.967249 | 0.193248 |
| precision | 1 | 0.960676 |
| recall | 1 | 0.00073441 |
| mcc | 0.934578 | 0.193248 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.120144 | nan |
| auc | 0.990847 | nan |
| f1 | 0.967462 | 0.193248 |
| accuracy | 0.967249 | 0.193248 |
| precision | 0.961207 | 0.193248 |
| recall | 0.973799 | 0.193248 |
| mcc | 0.934578 | 0.193248 |
Confusion matrix (at threshold=0.193248)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 6 | 223 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 17_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.9 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.123813 | nan |
| auc | 0.987834 | nan |
| f1 | 0.963124 | 0.248975 |
| accuracy | 0.962882 | 0.248975 |
| precision | 1 | 0.972835 |
| recall | 1 | 0.000309712 |
| mcc | 0.925844 | 0.248975 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.123813 | nan |
| auc | 0.987834 | nan |
| f1 | 0.963124 | 0.248975 |
| accuracy | 0.962882 | 0.248975 |
| precision | 0.956897 | 0.248975 |
| recall | 0.969432 | 0.248975 |
| mcc | 0.925844 | 0.248975 |
Confusion matrix (at threshold=0.248975)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 219 | 10 |
| Labeled as 1 | 7 | 222 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 17_CatBoost_RandomFeature
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.125614 | nan |
| auc | 0.988692 | nan |
| f1 | 0.964758 | 0.404373 |
| accuracy | 0.965066 | 0.404373 |
| precision | 1 | 0.973556 |
| recall | 1 | 0.000361544 |
| mcc | 0.930699 | 0.509142 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.125614 | nan |
| auc | 0.988692 | nan |
| f1 | 0.964758 | 0.404373 |
| accuracy | 0.965066 | 0.404373 |
| precision | 0.973333 | 0.404373 |
| recall | 0.956332 | 0.404373 |
| mcc | 0.930273 | 0.404373 |
Confusion matrix (at threshold=0.404373)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 17_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.11511 | nan |
| auc | 0.9918 | nan |
| f1 | 0.964758 | 0.531687 |
| accuracy | 0.965066 | 0.531687 |
| precision | 1 | 0.963362 |
| recall | 1 | 0.000647972 |
| mcc | 0.930273 | 0.531687 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.11511 | nan |
| auc | 0.9918 | nan |
| f1 | 0.964758 | 0.531687 |
| accuracy | 0.965066 | 0.531687 |
| precision | 0.973333 | 0.531687 |
| recall | 0.956332 | 0.531687 |
| mcc | 0.930273 | 0.531687 |
Confusion matrix (at threshold=0.531687)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 18_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 8
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.9 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.128354 | nan |
| auc | 0.989359 | nan |
| f1 | 0.964758 | 0.341593 |
| accuracy | 0.965066 | 0.341593 |
| precision | 1 | 0.966142 |
| recall | 1 | 5.41071e-05 |
| mcc | 0.930273 | 0.341593 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.128354 | nan |
| auc | 0.989359 | nan |
| f1 | 0.964758 | 0.341593 |
| accuracy | 0.965066 | 0.341593 |
| precision | 0.973333 | 0.341593 |
| recall | 0.956332 | 0.341593 |
| mcc | 0.930273 | 0.341593 |
Confusion matrix (at threshold=0.341593)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 19_RandomForest
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.197129 | nan |
| auc | 0.984993 | nan |
| f1 | 0.951965 | 0.500505 |
| accuracy | 0.951965 | 0.500505 |
| precision | 1 | 0.861863 |
| recall | 1 | 0.0191015 |
| mcc | 0.905174 | 0.565289 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.197129 | nan |
| auc | 0.984993 | nan |
| f1 | 0.951965 | 0.500505 |
| accuracy | 0.951965 | 0.500505 |
| precision | 0.951965 | 0.500505 |
| recall | 0.951965 | 0.500505 |
| mcc | 0.90393 | 0.500505 |
Confusion matrix (at threshold=0.500505)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 218 | 11 |
| Labeled as 1 | 11 | 218 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 19_RandomForest_SelectedFeatures
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.21026 | nan |
| auc | 0.981446 | nan |
| f1 | 0.94382 | 0.571143 |
| accuracy | 0.945415 | 0.571143 |
| precision | 1 | 0.8038 |
| recall | 1 | 0.0191402 |
| mcc | 0.892269 | 0.571143 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.21026 | nan |
| auc | 0.981446 | nan |
| f1 | 0.94382 | 0.571143 |
| accuracy | 0.945415 | 0.571143 |
| precision | 0.972222 | 0.571143 |
| recall | 0.917031 | 0.571143 |
| mcc | 0.892269 | 0.571143 |
Confusion matrix (at threshold=0.571143)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 19 | 210 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 1_Linear
Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
13.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.192042 | nan |
| auc | 0.974028 | nan |
| f1 | 0.941176 | 0.538017 |
| accuracy | 0.943231 | 0.538017 |
| precision | 1 | 0.88594 |
| recall | 1 | 0.00697453 |
| mcc | 0.888635 | 0.538017 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.192042 | nan |
| auc | 0.974028 | nan |
| f1 | 0.941176 | 0.538017 |
| accuracy | 0.943231 | 0.538017 |
| precision | 0.976526 | 0.538017 |
| recall | 0.908297 | 0.538017 |
| mcc | 0.888635 | 0.538017 |
Confusion matrix (at threshold=0.538017)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 224 | 5 |
| Labeled as 1 | 21 | 208 |
Learning curves
Coefficients
| feature | Learner_1 | Learner_2 | Learner_3 | Learner_4 | Learner_5 |
|---|---|---|---|---|---|
| PEER_PRESSURE | 2.09855 | 1.97751 | 2.10596 | 2.24214 | 2.11845 |
| ALLERGY | 1.71851 | 1.8115 | 1.80494 | 1.6434 | 1.99987 |
| CHRONIC DISEASE | 1.87657 | 1.63242 | 1.72145 | 1.71654 | 1.80335 |
| YELLOW_FINGERS | 1.60769 | 1.59401 | 1.57961 | 1.70178 | 1.69653 |
| SWALLOWING DIFFICULTY | 1.54115 | 1.39416 | 1.52935 | 1.57504 | 1.50732 |
| COUGHING | 1.6269 | 1.44695 | 1.61924 | 1.30983 | 1.45889 |
| WHEEZING | 1.39098 | 1.33944 | 1.35351 | 1.6218 | 1.23305 |
| ALCOHOL CONSUMING | 1.2172 | 1.51097 | 1.01974 | 1.43756 | 1.6208 |
| FATIGUE | 1.22949 | 1.23666 | 1.24562 | 1.05542 | 1.27239 |
| ANXIETY | 0.676465 | 1.11655 | 0.820949 | 1.03973 | 0.927658 |
| CHEST PAIN | 0.451082 | 0.609851 | 0.301484 | 0.389733 | 0.68452 |
| SMOKING | 0.404751 | 0.352542 | 0.398707 | 0.489795 | 0.317827 |
| SHORTNESS OF BREATH | 0.222579 | 0.360146 | 0.254631 | 0.399099 | 0.113896 |
| AGE | -0.0683001 | 0.0651653 | 0.0293103 | -0.0196802 | 0.14053 |
| GENDER | -0.0705105 | -0.180772 | -0.240995 | -0.128562 | 0.147742 |
| intercept | -4.88958 | -5.06286 | -4.78165 | -5.16035 | -5.28215 |
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 20_RandomForest
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.7
- min_samples_split: 30
- max_depth: 7
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.7 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.230631 | nan |
| auc | 0.979796 | nan |
| f1 | 0.930337 | 0.606783 |
| accuracy | 0.932314 | 0.606783 |
| precision | 1 | 0.75881 |
| recall | 1 | 0 |
| mcc | 0.866025 | 0.606783 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.230631 | nan |
| auc | 0.979796 | nan |
| f1 | 0.930337 | 0.606783 |
| accuracy | 0.932314 | 0.606783 |
| precision | 0.958333 | 0.606783 |
| recall | 0.90393 | 0.606783 |
| mcc | 0.866025 | 0.606783 |
Confusion matrix (at threshold=0.606783)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 22 | 207 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 21_RandomForest
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.8
- min_samples_split: 40
- max_depth: 3
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.281769 | nan |
| auc | 0.956637 | nan |
| f1 | 0.894309 | 0.439612 |
| accuracy | 0.895197 | 0.608282 |
| precision | 1 | 0.76644 |
| recall | 1 | 0.0344974 |
| mcc | 0.790514 | 0.608282 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.281769 | nan |
| auc | 0.956637 | nan |
| f1 | 0.894273 | 0.608282 |
| accuracy | 0.895197 | 0.608282 |
| precision | 0.902222 | 0.608282 |
| recall | 0.886463 | 0.608282 |
| mcc | 0.790514 | 0.608282 |
Confusion matrix (at threshold=0.608282)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 207 | 22 |
| Labeled as 1 | 26 | 203 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 22_RandomForest
Random Forest
- n_jobs: -1
- criterion: entropy
- max_features: 0.6
- min_samples_split: 50
- max_depth: 6
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.0 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.282002 | nan |
| auc | 0.968374 | nan |
| f1 | 0.908714 | 0.564196 |
| accuracy | 0.90393 | 0.564196 |
| precision | 1 | 0.798962 |
| recall | 1 | 0 |
| mcc | 0.812334 | 0.564196 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.282002 | nan |
| auc | 0.968374 | nan |
| f1 | 0.908714 | 0.564196 |
| accuracy | 0.90393 | 0.564196 |
| precision | 0.865613 | 0.564196 |
| recall | 0.956332 | 0.564196 |
| mcc | 0.812334 | 0.564196 |
Confusion matrix (at threshold=0.564196)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 195 | 34 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 23_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 4
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
4.6 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.232964 | nan |
| auc | 0.976917 | nan |
| f1 | 0.944321 | 0.492837 |
| accuracy | 0.945415 | 0.492837 |
| precision | 1 | 0.99338 |
| recall | 1 | 6.41845e-12 |
| mcc | 0.891518 | 0.492837 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.232964 | nan |
| auc | 0.976917 | nan |
| f1 | 0.944321 | 0.492837 |
| accuracy | 0.945415 | 0.492837 |
| precision | 0.963636 | 0.492837 |
| recall | 0.925764 | 0.492837 |
| mcc | 0.891518 | 0.492837 |
Confusion matrix (at threshold=0.492837)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 17 | 212 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 24_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.192402 | nan |
| auc | 0.974066 | nan |
| f1 | 0.944321 | 0.509772 |
| accuracy | 0.945415 | 0.509772 |
| precision | 1 | 0.999026 |
| recall | 1 | 0.000268946 |
| mcc | 0.891518 | 0.509772 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.192402 | nan |
| auc | 0.974066 | nan |
| f1 | 0.944321 | 0.509772 |
| accuracy | 0.945415 | 0.509772 |
| precision | 0.963636 | 0.509772 |
| recall | 0.925764 | 0.509772 |
| mcc | 0.891518 | 0.509772 |
Confusion matrix (at threshold=0.509772)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 17 | 212 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 25_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 32
- learning_rate: 0.08
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
6.1 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.244839 | nan |
| auc | 0.966877 | nan |
| f1 | 0.938865 | 0.505872 |
| accuracy | 0.938865 | 0.505872 |
| precision | 1 | 0.991168 |
| recall | 1 | 4.76261e-06 |
| mcc | 0.877863 | 0.551479 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.244839 | nan |
| auc | 0.966877 | nan |
| f1 | 0.938865 | 0.505872 |
| accuracy | 0.938865 | 0.505872 |
| precision | 0.938865 | 0.505872 |
| recall | 0.938865 | 0.505872 |
| mcc | 0.877729 | 0.505872 |
Confusion matrix (at threshold=0.505872)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 215 | 14 |
| Labeled as 1 | 14 | 215 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 26_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 32
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
6.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.190663 | nan |
| auc | 0.979119 | nan |
| f1 | 0.947598 | 0.46477 |
| accuracy | 0.947598 | 0.46477 |
| precision | 1 | 0.999892 |
| recall | 1 | 4.76882e-06 |
| mcc | 0.895197 | 0.46477 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.190663 | nan |
| auc | 0.979119 | nan |
| f1 | 0.947598 | 0.46477 |
| accuracy | 0.947598 | 0.46477 |
| precision | 0.947598 | 0.46477 |
| recall | 0.947598 | 0.46477 |
| mcc | 0.895197 | 0.46477 |
Confusion matrix (at threshold=0.46477)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 217 | 12 |
| Labeled as 1 | 12 | 217 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 26_NeuralNetwork_SelectedFeatures
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 32
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.159097 | nan |
| auc | 0.985736 | nan |
| f1 | 0.948052 | 0.243153 |
| accuracy | 0.947598 | 0.243153 |
| precision | 1 | 0.970249 |
| recall | 1 | 4.80733e-07 |
| mcc | 0.896428 | 0.564547 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.159097 | nan |
| auc | 0.985736 | nan |
| f1 | 0.948052 | 0.243153 |
| accuracy | 0.947598 | 0.243153 |
| precision | 0.939914 | 0.243153 |
| recall | 0.956332 | 0.243153 |
| mcc | 0.895333 | 0.243153 |
Confusion matrix (at threshold=0.243153)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 215 | 14 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 27_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.1 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.115485 | nan |
| auc | 0.991018 | nan |
| f1 | 0.965066 | 0.315008 |
| accuracy | 0.965066 | 0.315008 |
| precision | 1 | 0.956766 |
| recall | 1 | 0.000491067 |
| mcc | 0.930273 | 0.606753 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.115485 | nan |
| auc | 0.991018 | nan |
| f1 | 0.965066 | 0.315008 |
| accuracy | 0.965066 | 0.315008 |
| precision | 0.965066 | 0.315008 |
| recall | 0.965066 | 0.315008 |
| mcc | 0.930131 | 0.315008 |
Confusion matrix (at threshold=0.315008)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 8 | 221 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 28_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.12736 | nan |
| auc | 0.989073 | nan |
| f1 | 0.962801 | 0.335322 |
| accuracy | 0.962882 | 0.335322 |
| precision | 1 | 0.980913 |
| recall | 1 | 0.000488153 |
| mcc | 0.925773 | 0.335322 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.12736 | nan |
| auc | 0.989073 | nan |
| f1 | 0.962801 | 0.335322 |
| accuracy | 0.962882 | 0.335322 |
| precision | 0.964912 | 0.335322 |
| recall | 0.960699 | 0.335322 |
| mcc | 0.925773 | 0.335322 |
Confusion matrix (at threshold=0.335322)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 29_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.120864 | nan |
| auc | 0.988959 | nan |
| f1 | 0.965368 | 0.204555 |
| accuracy | 0.965066 | 0.204555 |
| precision | 1 | 0.941545 |
| recall | 1 | 0.000282356 |
| mcc | 0.930273 | 0.204555 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.120864 | nan |
| auc | 0.988959 | nan |
| f1 | 0.965368 | 0.204555 |
| accuracy | 0.965066 | 0.204555 |
| precision | 0.957082 | 0.204555 |
| recall | 0.973799 | 0.204555 |
| mcc | 0.930273 | 0.204555 |
Confusion matrix (at threshold=0.204555)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 219 | 10 |
| Labeled as 1 | 6 | 223 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 2_Default_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.13902 | nan |
| auc | 0.984497 | nan |
| f1 | 0.960699 | 0.395116 |
| accuracy | 0.960699 | 0.395116 |
| precision | 1 | 0.984284 |
| recall | 1 | 0.00157081 |
| mcc | 0.921397 | 0.395116 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.13902 | nan |
| auc | 0.984497 | nan |
| f1 | 0.960699 | 0.395116 |
| accuracy | 0.960699 | 0.395116 |
| precision | 0.960699 | 0.395116 |
| recall | 0.960699 | 0.395116 |
| mcc | 0.921397 | 0.395116 |
Confusion matrix (at threshold=0.395116)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 30_LightGBM_SelectedFeatures
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 31
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.126824 | nan |
| auc | 0.987319 | nan |
| f1 | 0.965368 | 0.251195 |
| accuracy | 0.965066 | 0.251195 |
| precision | 1 | 0.991555 |
| recall | 1 | 4.46669e-05 |
| mcc | 0.930273 | 0.251195 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.126824 | nan |
| auc | 0.987319 | nan |
| f1 | 0.965368 | 0.251195 |
| accuracy | 0.965066 | 0.251195 |
| precision | 0.957082 | 0.251195 |
| recall | 0.973799 | 0.251195 |
| mcc | 0.930273 | 0.251195 |
Confusion matrix (at threshold=0.251195)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 219 | 10 |
| Labeled as 1 | 6 | 223 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 31_LightGBM_SelectedFeatures
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 95
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.126824 | nan |
| auc | 0.987319 | nan |
| f1 | 0.965368 | 0.251195 |
| accuracy | 0.965066 | 0.251195 |
| precision | 1 | 0.991555 |
| recall | 1 | 4.46679e-05 |
| mcc | 0.930273 | 0.251195 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.126824 | nan |
| auc | 0.987319 | nan |
| f1 | 0.965368 | 0.251195 |
| accuracy | 0.965066 | 0.251195 |
| precision | 0.957082 | 0.251195 |
| recall | 0.973799 | 0.251195 |
| mcc | 0.930273 | 0.251195 |
Confusion matrix (at threshold=0.251195)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 219 | 10 |
| Labeled as 1 | 6 | 223 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 32_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 31
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.130602 | nan |
| auc | 0.985832 | nan |
| f1 | 0.96 | 0.66659 |
| accuracy | 0.960699 | 0.66659 |
| precision | 1 | 0.993058 |
| recall | 1 | 0.000106007 |
| mcc | 0.92196 | 0.66659 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.130602 | nan |
| auc | 0.985832 | nan |
| f1 | 0.96 | 0.66659 |
| accuracy | 0.960699 | 0.66659 |
| precision | 0.977376 | 0.66659 |
| recall | 0.943231 | 0.66659 |
| mcc | 0.92196 | 0.66659 |
Confusion matrix (at threshold=0.66659)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 224 | 5 |
| Labeled as 1 | 13 | 216 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 33_LightGBM
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 95
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.130602 | nan |
| auc | 0.985832 | nan |
| f1 | 0.96 | 0.66659 |
| accuracy | 0.960699 | 0.66659 |
| precision | 1 | 0.993058 |
| recall | 1 | 0.000106007 |
| mcc | 0.92196 | 0.66659 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.130602 | nan |
| auc | 0.985832 | nan |
| f1 | 0.96 | 0.66659 |
| accuracy | 0.960699 | 0.66659 |
| precision | 0.977376 | 0.66659 |
| recall | 0.943231 | 0.66659 |
| mcc | 0.92196 | 0.66659 |
Confusion matrix (at threshold=0.66659)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 224 | 5 |
| Labeled as 1 | 13 | 216 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 34_Xgboost_SelectedFeatures
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.05
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.7 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.138084 | nan |
| auc | 0.985078 | nan |
| f1 | 0.960699 | 0.344353 |
| accuracy | 0.960699 | 0.344353 |
| precision | 1 | 0.969609 |
| recall | 1 | 0.00120769 |
| mcc | 0.921397 | 0.344353 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.138084 | nan |
| auc | 0.985078 | nan |
| f1 | 0.960699 | 0.344353 |
| accuracy | 0.960699 | 0.344353 |
| precision | 0.960699 | 0.344353 |
| recall | 0.960699 | 0.344353 |
| mcc | 0.921397 | 0.344353 |
Confusion matrix (at threshold=0.344353)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 35_Xgboost_SelectedFeatures
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.1
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.13483 | nan |
| auc | 0.985527 | nan |
| f1 | 0.960699 | 0.322268 |
| accuracy | 0.960699 | 0.322268 |
| precision | 1 | 0.983956 |
| recall | 1 | 0.0008544 |
| mcc | 0.921397 | 0.322268 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.13483 | nan |
| auc | 0.985527 | nan |
| f1 | 0.960699 | 0.322268 |
| accuracy | 0.960699 | 0.322268 |
| precision | 0.960699 | 0.322268 |
| recall | 0.960699 | 0.322268 |
| mcc | 0.921397 | 0.322268 |
Confusion matrix (at threshold=0.322268)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 36_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.05
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.7 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.136081 | nan |
| auc | 0.984545 | nan |
| f1 | 0.960699 | 0.377673 |
| accuracy | 0.960699 | 0.377673 |
| precision | 1 | 0.988724 |
| recall | 1 | 0.00071005 |
| mcc | 0.92196 | 0.592465 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.136081 | nan |
| auc | 0.984545 | nan |
| f1 | 0.960699 | 0.377673 |
| accuracy | 0.960699 | 0.377673 |
| precision | 0.960699 | 0.377673 |
| recall | 0.960699 | 0.377673 |
| mcc | 0.921397 | 0.377673 |
Confusion matrix (at threshold=0.377673)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 37_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.1
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.137424 | nan |
| auc | 0.984163 | nan |
| f1 | 0.960352 | 0.510705 |
| accuracy | 0.960699 | 0.510705 |
| precision | 1 | 0.9901 |
| recall | 1 | 0.00118634 |
| mcc | 0.921538 | 0.510705 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.137424 | nan |
| auc | 0.984163 | nan |
| f1 | 0.960352 | 0.510705 |
| accuracy | 0.960699 | 0.510705 |
| precision | 0.968889 | 0.510705 |
| recall | 0.951965 | 0.510705 |
| mcc | 0.921538 | 0.510705 |
Confusion matrix (at threshold=0.510705)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 222 | 7 |
| Labeled as 1 | 11 | 218 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 38_NeuralNetwork_SelectedFeatures
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.189926 | nan |
| auc | 0.976011 | nan |
| f1 | 0.943723 | 0.300079 |
| accuracy | 0.943231 | 0.300079 |
| precision | 1 | 0.972728 |
| recall | 1 | 1.98825e-05 |
| mcc | 0.886598 | 0.300079 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.189926 | nan |
| auc | 0.976011 | nan |
| f1 | 0.943723 | 0.300079 |
| accuracy | 0.943231 | 0.300079 |
| precision | 0.935622 | 0.300079 |
| recall | 0.951965 | 0.300079 |
| mcc | 0.886598 | 0.300079 |
Confusion matrix (at threshold=0.300079)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 214 | 15 |
| Labeled as 1 | 11 | 218 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 39_RandomForest
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.6
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
11.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.210377 | nan |
| auc | 0.979558 | nan |
| f1 | 0.933902 | 0.466153 |
| accuracy | 0.932314 | 0.466153 |
| precision | 1 | 0.824087 |
| recall | 1 | 0.0119462 |
| mcc | 0.866025 | 0.573177 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.210377 | nan |
| auc | 0.979558 | nan |
| f1 | 0.933902 | 0.466153 |
| accuracy | 0.932314 | 0.466153 |
| precision | 0.9125 | 0.466153 |
| recall | 0.956332 | 0.466153 |
| mcc | 0.865628 | 0.466153 |
Confusion matrix (at threshold=0.466153)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 208 | 21 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 3_Default_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.1365 | nan |
| auc | 0.98443 | nan |
| f1 | 0.960352 | 0.476179 |
| accuracy | 0.960699 | 0.476179 |
| precision | 1 | 0.989747 |
| recall | 1 | 0.000484369 |
| mcc | 0.921538 | 0.476179 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.1365 | nan |
| auc | 0.98443 | nan |
| f1 | 0.960352 | 0.476179 |
| accuracy | 0.960699 | 0.476179 |
| precision | 0.968889 | 0.476179 |
| recall | 0.951965 | 0.476179 |
| mcc | 0.921538 | 0.476179 |
Confusion matrix (at threshold=0.476179)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 222 | 7 |
| Labeled as 1 | 11 | 218 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 3_Default_Xgboost_SelectedFeatures
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
8.0 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.135841 | nan |
| auc | 0.985422 | nan |
| f1 | 0.958785 | 0.240466 |
| accuracy | 0.958515 | 0.240466 |
| precision | 1 | 0.982293 |
| recall | 1 | 0.0020784 |
| mcc | 0.917109 | 0.240466 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.135841 | nan |
| auc | 0.985422 | nan |
| f1 | 0.958785 | 0.240466 |
| accuracy | 0.958515 | 0.240466 |
| precision | 0.952586 | 0.240466 |
| recall | 0.965066 | 0.240466 |
| mcc | 0.917109 | 0.240466 |
Confusion matrix (at threshold=0.240466)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 218 | 11 |
| Labeled as 1 | 8 | 221 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 40_RandomForest_SelectedFeatures
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.6
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.7 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.211415 | nan |
| auc | 0.981341 | nan |
| f1 | 0.938326 | 0.546339 |
| accuracy | 0.938865 | 0.546339 |
| precision | 1 | 0.761865 |
| recall | 1 | 0.0138488 |
| mcc | 0.880453 | 0.591041 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.211415 | nan |
| auc | 0.981341 | nan |
| f1 | 0.938326 | 0.546339 |
| accuracy | 0.938865 | 0.546339 |
| precision | 0.946667 | 0.546339 |
| recall | 0.930131 | 0.546339 |
| mcc | 0.877863 | 0.546339 |
Confusion matrix (at threshold=0.546339)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 217 | 12 |
| Labeled as 1 | 16 | 213 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 41_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 7
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.114777 | nan |
| auc | 0.992353 | nan |
| f1 | 0.964758 | 0.457111 |
| accuracy | 0.965066 | 0.457111 |
| precision | 1 | 0.944183 |
| recall | 1 | 0.000695863 |
| mcc | 0.930273 | 0.457111 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.114777 | nan |
| auc | 0.992353 | nan |
| f1 | 0.964758 | 0.457111 |
| accuracy | 0.965066 | 0.457111 |
| precision | 0.973333 | 0.457111 |
| recall | 0.956332 | 0.457111 |
| mcc | 0.930273 | 0.457111 |
Confusion matrix (at threshold=0.457111)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 42_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 7
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.6 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.122772 | nan |
| auc | 0.990427 | nan |
| f1 | 0.961039 | 0.212451 |
| accuracy | 0.960699 | 0.212451 |
| precision | 1 | 0.978081 |
| recall | 1 | 0.000435529 |
| mcc | 0.92196 | 0.592306 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.122772 | nan |
| auc | 0.990427 | nan |
| f1 | 0.961039 | 0.212451 |
| accuracy | 0.960699 | 0.212451 |
| precision | 0.95279 | 0.212451 |
| recall | 0.969432 | 0.212451 |
| mcc | 0.921538 | 0.212451 |
Confusion matrix (at threshold=0.212451)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 218 | 11 |
| Labeled as 1 | 7 | 222 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 43_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 6
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.118655 | nan |
| auc | 0.990199 | nan |
| f1 | 0.964758 | 0.497333 |
| accuracy | 0.965066 | 0.497333 |
| precision | 1 | 0.943056 |
| recall | 1 | 0.000509501 |
| mcc | 0.930273 | 0.497333 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.118655 | nan |
| auc | 0.990199 | nan |
| f1 | 0.964758 | 0.497333 |
| accuracy | 0.965066 | 0.497333 |
| precision | 0.973333 | 0.497333 |
| recall | 0.956332 | 0.497333 |
| mcc | 0.930273 | 0.497333 |
Confusion matrix (at threshold=0.497333)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 10 | 219 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 44_CatBoost_SelectedFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 6
- rsm: 1.0
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.111754 | nan |
| auc | 0.991457 | nan |
| f1 | 0.965665 | 0.137019 |
| accuracy | 0.965066 | 0.137019 |
| precision | 1 | 0.967072 |
| recall | 1 | 0.000444991 |
| mcc | 0.930699 | 0.137019 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.111754 | nan |
| auc | 0.991457 | nan |
| f1 | 0.965665 | 0.137019 |
| accuracy | 0.965066 | 0.137019 |
| precision | 0.949367 | 0.137019 |
| recall | 0.982533 | 0.137019 |
| mcc | 0.930699 | 0.137019 |
Confusion matrix (at threshold=0.137019)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 217 | 12 |
| Labeled as 1 | 4 | 225 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 45_LightGBM_SelectedFeatures
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 31
- learning_rate: 0.2
- feature_fraction: 0.8
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.0 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.130249 | nan |
| auc | 0.98608 | nan |
| f1 | 0.960699 | 0.358905 |
| accuracy | 0.960699 | 0.358905 |
| precision | 1 | 0.975855 |
| recall | 1 | 0.000522998 |
| mcc | 0.922665 | 0.672021 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.130249 | nan |
| auc | 0.98608 | nan |
| f1 | 0.960699 | 0.358905 |
| accuracy | 0.960699 | 0.358905 |
| precision | 0.960699 | 0.358905 |
| recall | 0.960699 | 0.358905 |
| mcc | 0.921397 | 0.358905 |
Confusion matrix (at threshold=0.358905)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 46_LightGBM_SelectedFeatures
LightGBM
- n_jobs: -1
- objective: binary
- num_leaves: 63
- learning_rate: 0.2
- feature_fraction: 0.8
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: binary_logloss
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.130249 | nan |
| auc | 0.986061 | nan |
| f1 | 0.960699 | 0.358905 |
| accuracy | 0.960699 | 0.358905 |
| precision | 1 | 0.975855 |
| recall | 1 | 0.000523004 |
| mcc | 0.922665 | 0.672021 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.130249 | nan |
| auc | 0.986061 | nan |
| f1 | 0.960699 | 0.358905 |
| accuracy | 0.960699 | 0.358905 |
| precision | 0.960699 | 0.358905 |
| recall | 0.960699 | 0.358905 |
| mcc | 0.921397 | 0.358905 |
Confusion matrix (at threshold=0.358905)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 47_Xgboost_SelectedFeatures
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.1
- max_depth: 6
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
11.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.165658 | nan |
| auc | 0.981084 | nan |
| f1 | 0.953229 | 0.516514 |
| accuracy | 0.954148 | 0.516514 |
| precision | 1 | 0.973176 |
| recall | 1 | 0.00143559 |
| mcc | 0.908999 | 0.516514 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.165658 | nan |
| auc | 0.981084 | nan |
| f1 | 0.953229 | 0.516514 |
| accuracy | 0.954148 | 0.516514 |
| precision | 0.972727 | 0.516514 |
| recall | 0.934498 | 0.516514 |
| mcc | 0.908999 | 0.516514 |
Confusion matrix (at threshold=0.516514)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 15 | 214 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 48_Xgboost_SelectedFeatures
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.3 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.163618 | nan |
| auc | 0.982132 | nan |
| f1 | 0.955556 | 0.528121 |
| accuracy | 0.956332 | 0.528121 |
| precision | 1 | 0.965852 |
| recall | 1 | 0.00110112 |
| mcc | 0.913221 | 0.528121 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.163618 | nan |
| auc | 0.982132 | nan |
| f1 | 0.955556 | 0.528121 |
| accuracy | 0.956332 | 0.528121 |
| precision | 0.972851 | 0.528121 |
| recall | 0.938865 | 0.528121 |
| mcc | 0.913221 | 0.528121 |
Confusion matrix (at threshold=0.528121)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 223 | 6 |
| Labeled as 1 | 14 | 215 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 49_NeuralNetwork_SelectedFeatures
Neural Network
- n_jobs: -1
- dense_1_size: 16
- dense_2_size: 32
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.7 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.203407 | nan |
| auc | 0.979463 | nan |
| f1 | 0.96 | 0.467738 |
| accuracy | 0.960699 | 0.467738 |
| precision | 1 | 0.999848 |
| recall | 1 | 6.37533e-17 |
| mcc | 0.92196 | 0.467738 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.203407 | nan |
| auc | 0.979463 | nan |
| f1 | 0.96 | 0.467738 |
| accuracy | 0.960699 | 0.467738 |
| precision | 0.977376 | 0.467738 |
| recall | 0.943231 | 0.467738 |
| mcc | 0.92196 | 0.467738 |
Confusion matrix (at threshold=0.467738)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 224 | 5 |
| Labeled as 1 | 13 | 216 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 4_Default_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
4.5 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.125332 | nan |
| auc | 0.988654 | nan |
| f1 | 0.961039 | 0.21499 |
| accuracy | 0.960699 | 0.21499 |
| precision | 1 | 0.948345 |
| recall | 1 | 0.000334563 |
| mcc | 0.921538 | 0.21499 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.125332 | nan |
| auc | 0.988654 | nan |
| f1 | 0.961039 | 0.21499 |
| accuracy | 0.960699 | 0.21499 |
| precision | 0.95279 | 0.21499 |
| recall | 0.969432 | 0.21499 |
| mcc | 0.921538 | 0.21499 |
Confusion matrix (at threshold=0.21499)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 218 | 11 |
| Labeled as 1 | 7 | 222 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 4_Default_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: Logloss
- eval_metric: Logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.4 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.123543 | nan |
| auc | 0.988101 | nan |
| f1 | 0.965066 | 0.324238 |
| accuracy | 0.965066 | 0.324238 |
| precision | 1 | 0.972491 |
| recall | 1 | 0.000416915 |
| mcc | 0.930131 | 0.324238 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.123543 | nan |
| auc | 0.988101 | nan |
| f1 | 0.965066 | 0.324238 |
| accuracy | 0.965066 | 0.324238 |
| precision | 0.965066 | 0.324238 |
| recall | 0.965066 | 0.324238 |
| mcc | 0.930131 | 0.324238 |
Confusion matrix (at threshold=0.324238)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 8 | 221 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 50_NeuralNetwork_SelectedFeatures
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 32
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.178132 | nan |
| auc | 0.979024 | nan |
| f1 | 0.942222 | 0.664605 |
| accuracy | 0.943231 | 0.664605 |
| precision | 1 | 0.957771 |
| recall | 1 | 1.52079e-05 |
| mcc | 0.887004 | 0.664605 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.178132 | nan |
| auc | 0.979024 | nan |
| f1 | 0.942222 | 0.664605 |
| accuracy | 0.943231 | 0.664605 |
| precision | 0.959276 | 0.664605 |
| recall | 0.925764 | 0.664605 |
| mcc | 0.887004 | 0.664605 |
Confusion matrix (at threshold=0.664605)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 17 | 212 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 51_NeuralNetwork_SelectedFeatures
Neural Network
- n_jobs: -1
- dense_1_size: 16
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.6 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.249332 | nan |
| auc | 0.964112 | nan |
| f1 | 0.934498 | 0.453542 |
| accuracy | 0.934498 | 0.453542 |
| precision | 1 | 0.99398 |
| recall | 1 | 1.31145e-05 |
| mcc | 0.869128 | 0.489579 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.249332 | nan |
| auc | 0.964112 | nan |
| f1 | 0.934498 | 0.453542 |
| accuracy | 0.934498 | 0.453542 |
| precision | 0.934498 | 0.453542 |
| recall | 0.934498 | 0.453542 |
| mcc | 0.868996 | 0.453542 |
Confusion matrix (at threshold=0.453542)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 214 | 15 |
| Labeled as 1 | 15 | 214 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 52_NeuralNetwork_SelectedFeatures
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
10.0 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.206625 | nan |
| auc | 0.974428 | nan |
| f1 | 0.946667 | 0.506958 |
| accuracy | 0.947598 | 0.506958 |
| precision | 1 | 0.986886 |
| recall | 1 | 1.2314e-07 |
| mcc | 0.895743 | 0.506958 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.206625 | nan |
| auc | 0.974428 | nan |
| f1 | 0.946667 | 0.506958 |
| accuracy | 0.947598 | 0.506958 |
| precision | 0.963801 | 0.506958 |
| recall | 0.930131 | 0.506958 |
| mcc | 0.895743 | 0.506958 |
Confusion matrix (at threshold=0.506958)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 16 | 213 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 53_RandomForest
Random Forest
- n_jobs: -1
- criterion: entropy
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
14.1 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.198354 | nan |
| auc | 0.982933 | nan |
| f1 | 0.946667 | 0.524126 |
| accuracy | 0.947598 | 0.524126 |
| precision | 1 | 0.832673 |
| recall | 1 | 0.00586375 |
| mcc | 0.896428 | 0.543011 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.198354 | nan |
| auc | 0.982933 | nan |
| f1 | 0.946667 | 0.524126 |
| accuracy | 0.947598 | 0.524126 |
| precision | 0.963801 | 0.524126 |
| recall | 0.930131 | 0.524126 |
| mcc | 0.895743 | 0.524126 |
Confusion matrix (at threshold=0.524126)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 221 | 8 |
| Labeled as 1 | 16 | 213 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 54_RandomForest_SelectedFeatures
Random Forest
- n_jobs: -1
- criterion: entropy
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.206369 | nan |
| auc | 0.980674 | nan |
| f1 | 0.944812 | 0.534069 |
| accuracy | 0.945415 | 0.534069 |
| precision | 1 | 0.839682 |
| recall | 1 | 0.0067978 |
| mcc | 0.891042 | 0.534069 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.206369 | nan |
| auc | 0.980674 | nan |
| f1 | 0.944812 | 0.534069 |
| accuracy | 0.945415 | 0.534069 |
| precision | 0.955357 | 0.534069 |
| recall | 0.934498 | 0.534069 |
| mcc | 0.891042 | 0.534069 |
Confusion matrix (at threshold=0.534069)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 219 | 10 |
| Labeled as 1 | 15 | 214 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 5_Default_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
3.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.261498 | nan |
| auc | 0.969814 | nan |
| f1 | 0.942731 | 0.422963 |
| accuracy | 0.943231 | 0.422963 |
| precision | 1 | 0.999584 |
| recall | 1 | 2.82294e-13 |
| mcc | 0.886598 | 0.422963 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.261498 | nan |
| auc | 0.969814 | nan |
| f1 | 0.942731 | 0.422963 |
| accuracy | 0.943231 | 0.422963 |
| precision | 0.951111 | 0.422963 |
| recall | 0.934498 | 0.422963 |
| mcc | 0.886598 | 0.422963 |
Confusion matrix (at threshold=0.422963)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 218 | 11 |
| Labeled as 1 | 15 | 214 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 6_Default_RandomForest
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
6.8 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.236473 | nan |
| auc | 0.96907 | nan |
| f1 | 0.922737 | 0.543356 |
| accuracy | 0.923581 | 0.543356 |
| precision | 1 | 0.885363 |
| recall | 1 | 0.00967923 |
| mcc | 0.847364 | 0.543356 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.236473 | nan |
| auc | 0.96907 | nan |
| f1 | 0.922737 | 0.543356 |
| accuracy | 0.923581 | 0.543356 |
| precision | 0.933036 | 0.543356 |
| recall | 0.912664 | 0.543356 |
| mcc | 0.847364 | 0.543356 |
Confusion matrix (at threshold=0.543356)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 214 | 15 |
| Labeled as 1 | 20 | 209 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 7_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 8
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.6 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.171097 | nan |
| auc | 0.980778 | nan |
| f1 | 0.947598 | 0.415023 |
| accuracy | 0.947598 | 0.415023 |
| precision | 1 | 0.983892 |
| recall | 1 | 0.000973013 |
| mcc | 0.895197 | 0.415023 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.171097 | nan |
| auc | 0.980778 | nan |
| f1 | 0.947598 | 0.415023 |
| accuracy | 0.947598 | 0.415023 |
| precision | 0.947598 | 0.415023 |
| recall | 0.947598 | 0.415023 |
| mcc | 0.895197 | 0.415023 |
Confusion matrix (at threshold=0.415023)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 217 | 12 |
| Labeled as 1 | 12 | 217 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 8_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.1
- max_depth: 8
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
5.2 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.13694 | nan |
| auc | 0.984649 | nan |
| f1 | 0.960699 | 0.332493 |
| accuracy | 0.960699 | 0.332493 |
| precision | 1 | 0.981755 |
| recall | 1 | 0.000780601 |
| mcc | 0.921397 | 0.332493 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.13694 | nan |
| auc | 0.984649 | nan |
| f1 | 0.960699 | 0.332493 |
| accuracy | 0.960699 | 0.332493 |
| precision | 0.960699 | 0.332493 |
| recall | 0.960699 | 0.332493 |
| mcc | 0.921397 | 0.332493 |
Confusion matrix (at threshold=0.332493)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 220 | 9 |
| Labeled as 1 | 9 | 220 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of 9_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.1
- max_depth: 7
- min_child_weight: 25
- subsample: 0.9
- colsample_bytree: 0.6
- eval_metric: logloss
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
6.0 seconds
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.381057 | nan |
| auc | 0.924105 | nan |
| f1 | 0.874172 | 0.526634 |
| accuracy | 0.875546 | 0.526634 |
| precision | 0.9375 | 0.851656 |
| recall | 1 | 0.0626449 |
| mcc | 0.751271 | 0.526634 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.381057 | nan |
| auc | 0.924105 | nan |
| f1 | 0.874172 | 0.526634 |
| accuracy | 0.875546 | 0.526634 |
| precision | 0.883929 | 0.526634 |
| recall | 0.864629 | 0.526634 |
| mcc | 0.751271 | 0.526634 |
Confusion matrix (at threshold=0.526634)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 203 | 26 |
| Labeled as 1 | 31 | 198 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
Summary of Ensemble
Ensemble structure
| Model | Weight |
|---|---|
| 12_LightGBM_SelectedFeatures | 2 |
| 26_NeuralNetwork_SelectedFeatures | 8 |
| 30_LightGBM_SelectedFeatures | 6 |
| 44_CatBoost_SelectedFeatures | 21 |
| 49_NeuralNetwork_SelectedFeatures | 3 |
| 50_NeuralNetwork_SelectedFeatures | 1 |
Metric details
| score | threshold | |
|---|---|---|
| logloss | 0.10497 | nan |
| auc | 0.993745 | nan |
| f1 | 0.961702 | 0.154249 |
| accuracy | 0.960699 | 0.154249 |
| precision | 1 | 0.930698 |
| recall | 1 | 0.000247398 |
| mcc | 0.922665 | 0.154249 |
Metric details with threshold from accuracy metric
| score | threshold | |
|---|---|---|
| logloss | 0.10497 | nan |
| auc | 0.993745 | nan |
| f1 | 0.961702 | 0.154249 |
| accuracy | 0.960699 | 0.154249 |
| precision | 0.937759 | 0.154249 |
| recall | 0.9869 | 0.154249 |
| mcc | 0.922665 | 0.154249 |
Confusion matrix (at threshold=0.154249)
| Predicted as 0 | Predicted as 1 | |
|---|---|---|
| Labeled as 0 | 214 | 15 |
| Labeled as 1 | 3 | 226 |
Learning curves
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
